Tuesday, June 15, 2021

Wow! it is almost so easy to predict stock market using moving average.....

What is Moving Average & Exponential Moving Average (EMA)?

Moving average is a statistical calculation, which is used to analyse data on the basis of series of averages based on subsets of the original dataset of the preceding or historical period. EMA is a type of weighted moving average which gives higher weight to the recent data. EMA are mostly used in predicting the future trend of data taken over a considerable period of time at a particular point. For Ex. Closing price of Nifty or any share such as Reliance, Infosys etc...

Stock prices are based on random walk principle and subject to extreme fluctuations. Moving average helps in smoothening these fluctuations and bring out the trend in the data.

Further reading on moving average:

Moving average - Wikipedia

How Is Exponential Moving Average (EMA) Calculated? (investopedia.com)

How to use Exponential Moving Average to predict breakout in stock prices?

The concept of moving average is based on the precise that it smoothens the fluctuation of data points based on average of data points of the preceding period. So longer the period of moving average more smooth the resultant trend will be. This will be more clear with a real time example of Nifty Chart for the last 3-year period.

The above chart is daily closing price of Nifty from the period Jan 2018.

Red trend line (other than arrows) is 9 days moving average of Nifty closing price and hence moving very close to the closing price of Nifty

Blue line is 30 days moving average of Nifty closing price and hence little smoother than 9 days MA

Green line is 100 days moving average of Nifty closing price and smoothens most of the recent fluctuations.

To predict breakout, we can follow one thumb rule i.e. whenever there is convergence of all 3 moving average i.e. 9 day, 30 days and 100 days MA the stock prices have consolidated and now its time to breakout. In our above example chart the first red arrow from the left indicate the convergence of all 3 MA in April 2018 and we can clearly see breakout from that point. Similar convergence is seen more recently in April 2020 and a clear breakout has happened after that. There is more such breakout in between which can be easily identified and strategy can be made accordingly.

How to use Moving Average for better decision in stock market trading?

We have already understood how convergence of moving average of different periods can help us identifying consolidation and breakouts. Similarly, these moving averages can be used to draw further insights.

·         Intersection of a higher period moving average by a lower period moving average is an indication of trend reversal.

·         If lower period moving average intersects higher period moving average from below then it’s a buying signal and if intersects from above, then it’s a sell signal. In the above example in early July 2020 first indication is given by 9DMA (red line) intersecting 30DMA (blue line) from below is 1st buying signal then 9DMA intersects 100DMA from below 2nd buying signal and then finally 30DMA intersects 100DMA from below is the final buying signal.

·         Stock prices trading above or below the moving average also indicates buy or sell signal. It is always a good time to buy when the stock prices have positive breakout above moving average during the trading hour and vice versa for sell.

·         For short term always buy stock above 30DMA and exit when breached. For long term buy above 100 DMA. In the above chart we can clearly identify such trend into picture.

·         Moving average can also be used for any type of trading decision such as day trading, short term or long term. We just need to adjust the period of chart data. For intraday it is better to take 1-minute chart. For short term hourly chart and for long term daily or weekly chart. Rest all analysis and inferences will be similar.

Option Trading strategy using Moving average?

Apart from making buying, selling and holding decisions based on various levels of stock and their moving averages, it can also be used for creating profitable option trading strategies.

When we see that 9DMA, 30DMA and 100 DMA is converging we can clearly see that there is a consolidation in stock prices and a breakout is anticipated. During this period volatility is usually low and hence options are available at cheaper price. Since the direction of breakout cannot be predicted accurately best strategy would be to buy both call and put and then selling them subsequently when breakout occurs.

On 30th Jan 2019 Nifty closed at 10650, all 3 moving averages were converging during this period. Closing price of strike price 10650 put was Rs 195/nifty and call was Rs 222/nifty so a combined investment of Rs 417. By the end of next week after breakout the straddle’s price was trading between 485 to 515 I.e a profit of approx. 15%-20%. Similar profits could also be achieved in March 2019 expiry.

Another strategy would be to design a Bull Call Spread if positive breakout is anticipated. For example, buying call of strike price 10651 and selling call of strike price 10750. This strategy has limited loss and limited gain. In our above example Nifty 10750 strike price call was closed at 166 on 30th Jan 2019. Therefore, an investment of Rs (222-166) = 56 and when the nifty moved up after breakout max profit of 44 can be achieved and max loss is 56 only. Similar strategy can also be built using put if negative breakout is anticipated.

 

 

Disclaimer: The information on this blog is provided for information purpose only. It does not constitute any offer, recommendation or solicitation to any person to enter into any transaction or adopt any hedging, trading or investment strategy, nor does it constitute any prediction of likely future movement in rates or prices.

 

 

 

6 comments:

Predicting Stock Prices: The Surprising Accuracy and Hidden Power of Linear Regression

Introduction to Linear Regression Linear regression is one of the most fundamental and widely used statistical techniques in data an...